, \(f\)  is a non-linear activation function, \(\mathbf {w} \in \mathbb {R}^{d \times m}\)  and \(b \in \mathbb {R}^{d}\)  are learned. We apply the GCN recurrence for \(K=3\)  steps.
At every step, node representations are re-computed using the representation of the nodes's neighbors from the previous step, see [1]} for details.
